EEG microstates are correlated with brain functional networks during slow -wave sleep
EEG microstates are correlated with brain functional networks during slow -wave sleep
复制标题
脑电图微状态与慢波睡眠期间的大脑功能网络相关
DOI：
10.1016/j.neuroimage.2020.116786
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发表时间：
2020-07-15
期刊：
影响因子：
5.7
通讯作者：
Gao, Jia-Hong
中科院分区：
文献类型：
--
作者：
Xu, Jing;Pan, Yu;Gao, Jia-Hong
脑电图（EEG）的微观状态已被广泛研究清醒状态，并已被描述为“思想的原子”。以前的EEG研究发现了四种微观状态，即，微观状态A、B、C和D，这些状态在静息状态期间的整个寿命期间在参与者之间是一致的。同时使用EEG和功能性磁共振成像（fMRI）的研究提供了证据，在静息状态下的EEG微状态和fMRI网络之间的相关性。在非快速眼动（NREM）睡眠期间也发现了微状态。慢波睡眠（SWS）被认为是最具恢复性的睡眠阶段，并与睡眠的维持有关。然而，在SWS过程中EEG微状态和脑功能网络之间的关系尚未得到研究。在这项研究中，同时收集的EEG功能磁共振数据，在SWS测试EEG微状态和功能磁共振网络之间的对应关系。脑电微观状态的fMRI分析显示，四种微观状态中有三种与fMRI数据有显著相关性：1）颞中回和颞后回的fMRI波动与微态B正相关，2）颞中回和梭状回的fMRI信号与微态C负相关，3）枕叶的fMRI波动与微态D负相关，而扣带回前部和扣带回的fMRI信号与此微状态呈正相关。然后使用基于fMRI数据的组独立成分分析来评估功能性脑网络。组水平的空间相关性分析表明，fMRI听觉网络与微态B的fMRI激活图重叠，执行控制网络与微态C的fMRI失活图重叠，视觉和显著性网络与微态D的fMRI失活和激活图重叠。此外，受试者水平的空间相关性之间的一般线性模型（GLM）β地图的每一个microstate和个人地图的每一个组件产生的双重回归也表明，EEG microstate密切相关的脑功能网络使用功能磁共振成像测量SWS。结果表明，慢波刺激时脑电微状态与脑功能网络密切相关，提示脑电微状态为脑功能网络提供了重要的电生理基础。
Electroencephalography (EEG) microstates have been extensively studied in wakefulness and have been described as the “atoms of thought”. Previous studies of EEG have found four microstates, i.e., microstates A, B, C and D, that are consistent among participants across the lifespan during the resting state. Studies using simultaneous EEG and functional magnetic resonance imaging (fMRI) have provided evidence for correlations between EEG microstates and fMRI networks during the resting state. Microstates have also been found during non-rapid eye movement (NREM) sleep. Slow-wave sleep (SWS) is considered the most restorative sleep stage and has been associated with the maintenance of sleep. However, the relationship between EEG microstates and brain functional networks during SWS has not yet been investigated. In this study, simultaneous EEG-fMRI data were collected during SWS to test the correspondence between EEG microstates and fMRI networks. EEG microstate-informed fMRI analysis revealed that three out of the four microstates showed significant correlations with fMRI data: 1) fMRI fluctuations in the insula and posterior temporal gyrus positively correlated with microstate B, 2) fMRI signals in the middle temporal gyrus and fusiform gyrus negatively correlated with microstate C, and 3) fMRI fluctuations in the occipital lobe negatively correlated with microstate D, while fMRI signals in the anterior cingulate and cingulate gyrus positively correlated with this microstate. Functional brain networks were then assessed using group independent component analysis based on the fMRI data. The group-level spatial correlation analysis showed that the fMRI auditory network overlapped the fMRI activation map of microstate B, the executive control network overlapped the fMRI deactivation of microstate C, and the visual and salience networks overlapped the fMRI deactivation and activation maps of microstate D. In addition, the subject-level spatial correlations between the general linear model (GLM) beta map of each microstate and the individual maps of each component yielded by dual regression also showed that EEG microstates were closely associated with brain functional networks measured using fMRI during SWS. Overall, the results showed that EEG microstates were closely related to brain functional networks during SWS, which suggested that EEG microstates provide an important electrophysiological basis underlying brain functional networks.